{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 复合索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'one', 'one']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['one']*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': [0, 1, 2, 3, 4, 5, 6],\n",
       " 'b': [7, 6, 5, 4, 3, 2, 1],\n",
       " 'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],\n",
       " 'd': ['h', 'i', 'j', 'k', 'l', 'm', 'n']}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {\n",
    "    'a': list(range(7)),\n",
    "    'b': list(range(7,0,-1)),\n",
    "    'c': ['one']*3 + ['two']*4,\n",
    "    'd': list(string.ascii_lowercase[7:14]),\n",
    "}\n",
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>d</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  h\n",
       "1  1  6  one  i\n",
       "2  2  5  one  j\n",
       "3  3  4  two  k\n",
       "4  4  3  two  l\n",
       "5  5  2  two  m\n",
       "6  6  1  two  n"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame(dict)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>d</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>one</td>\n",
       "      <td>h</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c    d\n",
       "0  0.0  7.0  one    h\n",
       "7  NaN  NaN  NaN  NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reindex([0,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>k</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   b    c  d\n",
       "a           \n",
       "0  7  one  h\n",
       "1  6  one  i\n",
       "2  5  one  j\n",
       "3  4  two  k\n",
       "4  3  two  l\n",
       "5  2  two  m\n",
       "6  1  two  n"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.set_index(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64', name='a')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.set_index(\"a\").index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   a  b    c  d\n",
       "a              \n",
       "0  0  7  one  h\n",
       "1  1  6  one  i\n",
       "2  2  5  one  j\n",
       "3  3  4  two  k\n",
       "4  4  3  two  l\n",
       "5  5  2  two  m\n",
       "6  6  1  two  n"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.set_index(\"a\", drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  h\n",
       "1  1  6  one  i\n",
       "2  2  5  one  j\n",
       "3  3  4  two  k\n",
       "4  4  3  two  l\n",
       "5  5  2  two  m\n",
       "6  6  1  two  n"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    d\n",
       "one  h    0\n",
       "     i    1\n",
       "     j    2\n",
       "two  k    3\n",
       "     l    4\n",
       "     m    5\n",
       "     n    6\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = a.set_index([\"c\",\"d\"])[\"a\"]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('one', 'h'),\n",
       "            ('one', 'i'),\n",
       "            ('one', 'j'),\n",
       "            ('two', 'k'),\n",
       "            ('two', 'l'),\n",
       "            ('two', 'm'),\n",
       "            ('two', 'n')],\n",
       "           names=['c', 'd'])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"one\"][\"h\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"one\", \"h\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-40-0dfc948bfb08>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-40-0dfc948bfb08>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    X.[\"one\"]\u001b[0m\n\u001b[1;37m      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "X.[\"one\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.loc[\"one\"].loc[\"h\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d  c  \n",
       "h  one    0\n",
       "i  one    1\n",
       "j  one    2\n",
       "k  two    3\n",
       "l  two    4\n",
       "m  two    5\n",
       "n  two    6\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.swaplevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c\n",
       "one    0\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.swaplevel()[\"h\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设多条数据的内层索引都是`h`, 而我们都要取到, 交换索引的顺序然后取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([['one', 'two'], ['h', 'i', 'j', 'k', 'l', 'm', 'n']])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.index.levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d\n",
       "h    0\n",
       "i    1\n",
       "j    2\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.loc[\"one\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.loc[\"one\"].loc[\"h\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c\n",
       "one    0\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.swaplevel().loc[\"h\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    d\n",
       "one  h    0\n",
       "     i    1\n",
       "     j    2\n",
       "two  k    3\n",
       "     l    4\n",
       "     m    5\n",
       "     n    6\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = a.set_index([\"c\",\"d\"])[\"a\"]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d\n",
       "h    0\n",
       "i    1\n",
       "j    2\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.loc[\"one\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.loc[\"one\"].loc['h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c\n",
       "one    0\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.swaplevel().loc[\"h\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 动手\n",
    "\n",
    "1. 使用matplotlib呈现出店铺总数排名前10的国家\n",
    "2. 使用matplotlib呈现出每个中国每个城市的店铺数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State/Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Timezone</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47370-257954</td>\n",
       "      <td>Meritxell, 96</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>Av. Meritxell, 96</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>7</td>\n",
       "      <td>AD</td>\n",
       "      <td>AD500</td>\n",
       "      <td>376818720</td>\n",
       "      <td>GMT+1:00 Europe/Andorra</td>\n",
       "      <td>1.53</td>\n",
       "      <td>42.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>22331-212325</td>\n",
       "      <td>Ajman Drive Thru</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>1 Street 69, Al Jarf</td>\n",
       "      <td>Ajman</td>\n",
       "      <td>AJ</td>\n",
       "      <td>AE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GMT+04:00 Asia/Dubai</td>\n",
       "      <td>55.47</td>\n",
       "      <td>25.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>47089-256771</td>\n",
       "      <td>Dana Mall</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>Sheikh Khalifa Bin Zayed St.</td>\n",
       "      <td>Ajman</td>\n",
       "      <td>AJ</td>\n",
       "      <td>AE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GMT+04:00 Asia/Dubai</td>\n",
       "      <td>55.47</td>\n",
       "      <td>25.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>22126-218024</td>\n",
       "      <td>Twofour 54</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>Al Salam Street</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>AZ</td>\n",
       "      <td>AE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GMT+04:00 Asia/Dubai</td>\n",
       "      <td>54.38</td>\n",
       "      <td>24.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>17127-178586</td>\n",
       "      <td>Al Ain Tower</td>\n",
       "      <td>Licensed</td>\n",
       "      <td>Khaldiya Area, Abu Dhabi Island</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>AZ</td>\n",
       "      <td>AE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GMT+04:00 Asia/Dubai</td>\n",
       "      <td>54.54</td>\n",
       "      <td>24.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Brand  Store Number        Store Name Ownership Type  \\\n",
       "0  Starbucks  47370-257954     Meritxell, 96       Licensed   \n",
       "1  Starbucks  22331-212325  Ajman Drive Thru       Licensed   \n",
       "2  Starbucks  47089-256771         Dana Mall       Licensed   \n",
       "3  Starbucks  22126-218024        Twofour 54       Licensed   \n",
       "4  Starbucks  17127-178586      Al Ain Tower       Licensed   \n",
       "\n",
       "                    Street Address              City State/Province Country  \\\n",
       "0                Av. Meritxell, 96  Andorra la Vella              7      AD   \n",
       "1             1 Street 69, Al Jarf             Ajman             AJ      AE   \n",
       "2     Sheikh Khalifa Bin Zayed St.             Ajman             AJ      AE   \n",
       "3                  Al Salam Street         Abu Dhabi             AZ      AE   \n",
       "4  Khaldiya Area, Abu Dhabi Island         Abu Dhabi             AZ      AE   \n",
       "\n",
       "  Postcode Phone Number                 Timezone  Longitude  Latitude  \n",
       "0    AD500    376818720  GMT+1:00 Europe/Andorra       1.53     42.51  \n",
       "1      NaN          NaN     GMT+04:00 Asia/Dubai      55.47     25.42  \n",
       "2      NaN          NaN     GMT+04:00 Asia/Dubai      55.47     25.39  \n",
       "3      NaN          NaN     GMT+04:00 Asia/Dubai      54.38     24.48  \n",
       "4      NaN          NaN     GMT+04:00 Asia/Dubai      54.54     24.51  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "file_path = './starbucks_store_worldwide.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State/Province</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Timezone</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13607</td>\n",
       "      <td>13122</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "      <td>13608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2192</td>\n",
       "      <td>1337</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1464</td>\n",
       "      <td>1314</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1228</td>\n",
       "      <td>143</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KR</th>\n",
       "      <td>993</td>\n",
       "      <td>993</td>\n",
       "      <td>993</td>\n",
       "      <td>993</td>\n",
       "      <td>991</td>\n",
       "      <td>993</td>\n",
       "      <td>993</td>\n",
       "      <td>979</td>\n",
       "      <td>216</td>\n",
       "      <td>993</td>\n",
       "      <td>992</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CW</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AW</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MC</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Brand  Store Number  Store Name  Ownership Type  Street Address  \\\n",
       "Country                                                                    \n",
       "US       13608         13608       13608           13608           13608   \n",
       "CN        2734          2734        2734            2734            2734   \n",
       "CA        1468          1468        1468            1468            1468   \n",
       "JP        1237          1237        1237            1237            1237   \n",
       "KR         993           993         993             993             991   \n",
       "...        ...           ...         ...             ...             ...   \n",
       "CW           3             3           3               3               3   \n",
       "AW           3             3           3               3               3   \n",
       "MC           2             2           2               2               2   \n",
       "LU           2             2           2               2               2   \n",
       "AD           1             1           1               1               1   \n",
       "\n",
       "          City  State/Province  Postcode  Phone Number  Timezone  Longitude  \\\n",
       "Country                                                                       \n",
       "US       13608           13608     13607         13122     13608      13608   \n",
       "CN        2734            2734      2192          1337      2734       2734   \n",
       "CA        1468            1468      1464          1314      1468       1468   \n",
       "JP        1237            1237      1228           143      1237       1237   \n",
       "KR         993             993       979           216       993        992   \n",
       "...        ...             ...       ...           ...       ...        ...   \n",
       "CW           3               3         0             1         3          3   \n",
       "AW           3               3         0             3         3          3   \n",
       "MC           2               2         2             0         2          2   \n",
       "LU           2               2         2             0         2          2   \n",
       "AD           1               1         1             1         1          1   \n",
       "\n",
       "         Latitude  \n",
       "Country            \n",
       "US          13608  \n",
       "CN           2734  \n",
       "CA           1468  \n",
       "JP           1237  \n",
       "KR            992  \n",
       "...           ...  \n",
       "CW              3  \n",
       "AW              3  \n",
       "MC              2  \n",
       "LU              2  \n",
       "AD              1  \n",
       "\n",
       "[73 rows x 12 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=\"Country\").count().sort_values(by=\"Brand\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_data = df.groupby(by=\"Country\").count()[\"Brand\"].sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_x = store_data.index\n",
    "_y = store_data.values\n",
    "\n",
    "# 画图\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,8), dpi=80)\n",
    "plt.bar(_x, _y, width=0.6, color=\"red\")\n",
    "plt.xticks(range(len(_x)), _x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>Store Name</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State/Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>Timezone</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2091</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>22901-225145</td>\n",
       "      <td>北京西站第一咖啡店</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>丰台区, 北京西站通廊7-1号, 中关村南大街2号</td>\n",
       "      <td>北京市</td>\n",
       "      <td>11</td>\n",
       "      <td>CN</td>\n",
       "      <td>100073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GMT+08:00 Asia/Beijing</td>\n",
       "      <td>116.32</td>\n",
       "      <td>39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2092</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>32320-116537</td>\n",
       "      <td>北京华宇时尚店</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>海淀区, 数码大厦B座华宇时尚购物中心内, 蓝色港湾国际商区1座C1-3单元首层、</td>\n",
       "      <td>北京市</td>\n",
       "      <td>11</td>\n",
       "      <td>CN</td>\n",
       "      <td>100086</td>\n",
       "      <td>010-51626616</td>\n",
       "      <td>GMT+08:00 Asia/Beijing</td>\n",
       "      <td>116.32</td>\n",
       "      <td>39.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>32447-132306</td>\n",
       "      <td>北京蓝色港湾圣拉娜店</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>朝阳区朝阳公园路6号, 二层C1-3单元及二层阳台, 太阳宫中路12号</td>\n",
       "      <td>北京市</td>\n",
       "      <td>11</td>\n",
       "      <td>CN</td>\n",
       "      <td>100020</td>\n",
       "      <td>010-59056343</td>\n",
       "      <td>GMT+08:00 Asia/Beijing</td>\n",
       "      <td>116.47</td>\n",
       "      <td>39.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2094</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>17477-161286</td>\n",
       "      <td>北京太阳宫凯德嘉茂店</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>朝阳区, 太阳宫凯德嘉茂一层01-44/45号, 东三环北路27号</td>\n",
       "      <td>北京市</td>\n",
       "      <td>11</td>\n",
       "      <td>CN</td>\n",
       "      <td>100028</td>\n",
       "      <td>010-84150945</td>\n",
       "      <td>GMT+08:00 Asia/Beijing</td>\n",
       "      <td>116.45</td>\n",
       "      <td>39.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2095</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>24520-237564</td>\n",
       "      <td>北京东三环北店</td>\n",
       "      <td>Company Owned</td>\n",
       "      <td>朝阳区, 嘉铭中心大厦A座B1层024商铺, 金融大街7号</td>\n",
       "      <td>北京市</td>\n",
       "      <td>11</td>\n",
       "      <td>CN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GMT+08:00 Asia/Beijing</td>\n",
       "      <td>116.46</td>\n",
       "      <td>39.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Brand  Store Number  Store Name Ownership Type  \\\n",
       "2091  Starbucks  22901-225145   北京西站第一咖啡店  Company Owned   \n",
       "2092  Starbucks  32320-116537     北京华宇时尚店  Company Owned   \n",
       "2093  Starbucks  32447-132306  北京蓝色港湾圣拉娜店  Company Owned   \n",
       "2094  Starbucks  17477-161286  北京太阳宫凯德嘉茂店  Company Owned   \n",
       "2095  Starbucks  24520-237564     北京东三环北店  Company Owned   \n",
       "\n",
       "                                 Street Address City State/Province Country  \\\n",
       "2091                  丰台区, 北京西站通廊7-1号, 中关村南大街2号  北京市             11      CN   \n",
       "2092  海淀区, 数码大厦B座华宇时尚购物中心内, 蓝色港湾国际商区1座C1-3单元首层、  北京市             11      CN   \n",
       "2093        朝阳区朝阳公园路6号, 二层C1-3单元及二层阳台, 太阳宫中路12号  北京市             11      CN   \n",
       "2094          朝阳区, 太阳宫凯德嘉茂一层01-44/45号, 东三环北路27号  北京市             11      CN   \n",
       "2095              朝阳区, 嘉铭中心大厦A座B1层024商铺, 金融大街7号  北京市             11      CN   \n",
       "\n",
       "     Postcode  Phone Number                Timezone  Longitude  Latitude  \n",
       "2091   100073           NaN  GMT+08:00 Asia/Beijing     116.32     39.90  \n",
       "2092   100086  010-51626616  GMT+08:00 Asia/Beijing     116.32     39.97  \n",
       "2093   100020  010-59056343  GMT+08:00 Asia/Beijing     116.47     39.95  \n",
       "2094   100028  010-84150945  GMT+08:00 Asia/Beijing     116.45     39.97  \n",
       "2095      NaN           NaN  GMT+08:00 Asia/Beijing     116.46     39.93  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"Country\"]==\"CN\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_data = df.groupby(by=\"City\").count()[\"Brand\"].sort_values(ascending=False)[:25]\n",
    "\n",
    "_x = city_data.index\n",
    "_y = city_data.values\n",
    "\n",
    "# 画图\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "plt.figure(figsize=(20,8), dpi=80)\n",
    "plt.bar(_x, _y, width=0.6, color=\"blue\")\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "plt.figure(figsize=(20,8), dpi=80)\n",
    "plt.barh(_x, _y, height=0.3, color=\"orange\")\n",
    "plt.yticks(range(len(_x)), _x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 动手\n",
    "\n",
    "现在我们有全球排名靠前的10000本书的数据，那么请统计一下下面几个问题：\n",
    "- 不同年份书的数量\n",
    "- 不同年份书的平均评分情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>best_book_id</th>\n",
       "      <th>work_id</th>\n",
       "      <th>books_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>authors</th>\n",
       "      <th>original_publication_year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>...</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>work_ratings_count</th>\n",
       "      <th>work_text_reviews_count</th>\n",
       "      <th>ratings_1</th>\n",
       "      <th>ratings_2</th>\n",
       "      <th>ratings_3</th>\n",
       "      <th>ratings_4</th>\n",
       "      <th>ratings_5</th>\n",
       "      <th>image_url</th>\n",
       "      <th>small_image_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2792775</td>\n",
       "      <td>272</td>\n",
       "      <td>439023483</td>\n",
       "      <td>9.780439e+12</td>\n",
       "      <td>Suzanne Collins</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>The Hunger Games</td>\n",
       "      <td>...</td>\n",
       "      <td>4780653</td>\n",
       "      <td>4942365</td>\n",
       "      <td>155254</td>\n",
       "      <td>66715</td>\n",
       "      <td>127936</td>\n",
       "      <td>560092</td>\n",
       "      <td>1481305</td>\n",
       "      <td>2706317</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4640799</td>\n",
       "      <td>491</td>\n",
       "      <td>439554934</td>\n",
       "      <td>9.780440e+12</td>\n",
       "      <td>J.K. Rowling, Mary GrandPré</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Harry Potter and the Philosopher's Stone</td>\n",
       "      <td>...</td>\n",
       "      <td>4602479</td>\n",
       "      <td>4800065</td>\n",
       "      <td>75867</td>\n",
       "      <td>75504</td>\n",
       "      <td>101676</td>\n",
       "      <td>455024</td>\n",
       "      <td>1156318</td>\n",
       "      <td>3011543</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>41865</td>\n",
       "      <td>41865</td>\n",
       "      <td>3212258</td>\n",
       "      <td>226</td>\n",
       "      <td>316015849</td>\n",
       "      <td>9.780316e+12</td>\n",
       "      <td>Stephenie Meyer</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Twilight</td>\n",
       "      <td>...</td>\n",
       "      <td>3866839</td>\n",
       "      <td>3916824</td>\n",
       "      <td>95009</td>\n",
       "      <td>456191</td>\n",
       "      <td>436802</td>\n",
       "      <td>793319</td>\n",
       "      <td>875073</td>\n",
       "      <td>1355439</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2657</td>\n",
       "      <td>2657</td>\n",
       "      <td>3275794</td>\n",
       "      <td>487</td>\n",
       "      <td>61120081</td>\n",
       "      <td>9.780061e+12</td>\n",
       "      <td>Harper Lee</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "      <td>...</td>\n",
       "      <td>3198671</td>\n",
       "      <td>3340896</td>\n",
       "      <td>72586</td>\n",
       "      <td>60427</td>\n",
       "      <td>117415</td>\n",
       "      <td>446835</td>\n",
       "      <td>1001952</td>\n",
       "      <td>1714267</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4671</td>\n",
       "      <td>4671</td>\n",
       "      <td>245494</td>\n",
       "      <td>1356</td>\n",
       "      <td>743273567</td>\n",
       "      <td>9.780743e+12</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>The Great Gatsby</td>\n",
       "      <td>...</td>\n",
       "      <td>2683664</td>\n",
       "      <td>2773745</td>\n",
       "      <td>51992</td>\n",
       "      <td>86236</td>\n",
       "      <td>197621</td>\n",
       "      <td>606158</td>\n",
       "      <td>936012</td>\n",
       "      <td>947718</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  book_id  best_book_id  work_id  books_count       isbn        isbn13  \\\n",
       "0   1  2767052       2767052  2792775          272  439023483  9.780439e+12   \n",
       "1   2        3             3  4640799          491  439554934  9.780440e+12   \n",
       "2   3    41865         41865  3212258          226  316015849  9.780316e+12   \n",
       "3   4     2657          2657  3275794          487   61120081  9.780061e+12   \n",
       "4   5     4671          4671   245494         1356  743273567  9.780743e+12   \n",
       "\n",
       "                       authors  original_publication_year  \\\n",
       "0              Suzanne Collins                     2008.0   \n",
       "1  J.K. Rowling, Mary GrandPré                     1997.0   \n",
       "2              Stephenie Meyer                     2005.0   \n",
       "3                   Harper Lee                     1960.0   \n",
       "4          F. Scott Fitzgerald                     1925.0   \n",
       "\n",
       "                             original_title  ... ratings_count  \\\n",
       "0                          The Hunger Games  ...       4780653   \n",
       "1  Harry Potter and the Philosopher's Stone  ...       4602479   \n",
       "2                                  Twilight  ...       3866839   \n",
       "3                     To Kill a Mockingbird  ...       3198671   \n",
       "4                          The Great Gatsby  ...       2683664   \n",
       "\n",
       "  work_ratings_count  work_text_reviews_count  ratings_1  ratings_2  \\\n",
       "0            4942365                   155254      66715     127936   \n",
       "1            4800065                    75867      75504     101676   \n",
       "2            3916824                    95009     456191     436802   \n",
       "3            3340896                    72586      60427     117415   \n",
       "4            2773745                    51992      86236     197621   \n",
       "\n",
       "   ratings_3  ratings_4  ratings_5  \\\n",
       "0     560092    1481305    2706317   \n",
       "1     455024    1156318    3011543   \n",
       "2     793319     875073    1355439   \n",
       "3     446835    1001952    1714267   \n",
       "4     606158     936012     947718   \n",
       "\n",
       "                                           image_url  \\\n",
       "0  https://images.gr-assets.com/books/1447303603m...   \n",
       "1  https://images.gr-assets.com/books/1474154022m...   \n",
       "2  https://images.gr-assets.com/books/1361039443m...   \n",
       "3  https://images.gr-assets.com/books/1361975680m...   \n",
       "4  https://images.gr-assets.com/books/1490528560m...   \n",
       "\n",
       "                                     small_image_url  \n",
       "0  https://images.gr-assets.com/books/1447303603s...  \n",
       "1  https://images.gr-assets.com/books/1474154022s...  \n",
       "2  https://images.gr-assets.com/books/1361039443s...  \n",
       "3  https://images.gr-assets.com/books/1361975680s...  \n",
       "4  https://images.gr-assets.com/books/1490528560s...  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = './books.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   id                         10000 non-null  int64  \n",
      " 1   book_id                    10000 non-null  int64  \n",
      " 2   best_book_id               10000 non-null  int64  \n",
      " 3   work_id                    10000 non-null  int64  \n",
      " 4   books_count                10000 non-null  int64  \n",
      " 5   isbn                       9300 non-null   object \n",
      " 6   isbn13                     9415 non-null   float64\n",
      " 7   authors                    10000 non-null  object \n",
      " 8   original_publication_year  9979 non-null   float64\n",
      " 9   original_title             9415 non-null   object \n",
      " 10  title                      10000 non-null  object \n",
      " 11  language_code              8916 non-null   object \n",
      " 12  average_rating             10000 non-null  float64\n",
      " 13  ratings_count              10000 non-null  int64  \n",
      " 14  work_ratings_count         10000 non-null  int64  \n",
      " 15  work_text_reviews_count    10000 non-null  int64  \n",
      " 16  ratings_1                  10000 non-null  int64  \n",
      " 17  ratings_2                  10000 non-null  int64  \n",
      " 18  ratings_3                  10000 non-null  int64  \n",
      " 19  ratings_4                  10000 non-null  int64  \n",
      " 20  ratings_5                  10000 non-null  int64  \n",
      " 21  image_url                  10000 non-null  object \n",
      " 22  small_image_url            10000 non-null  object \n",
      "dtypes: float64(3), int64(13), object(7)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "original_publication_year\n",
       "-1750.0      1\n",
       "-762.0       1\n",
       "-750.0       2\n",
       "-720.0       1\n",
       "-560.0       1\n",
       "          ... \n",
       " 2013.0    518\n",
       " 2014.0    437\n",
       " 2015.0    306\n",
       " 2016.0    198\n",
       " 2017.0     11\n",
       "Name: title, Length: 293, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不同年份书的数量\n",
    "data = df[pd.notnull(df[\"original_publication_year\"])]\n",
    "num_grouped = data.groupby(by=\"original_publication_year\").count()[\"title\"]\n",
    "num_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "original_publication_year\n",
       "-1750.0    3.630000\n",
       "-762.0     4.030000\n",
       "-750.0     4.005000\n",
       "-720.0     3.730000\n",
       "-560.0     4.050000\n",
       "             ...   \n",
       " 2013.0    4.012297\n",
       " 2014.0    3.985378\n",
       " 2015.0    3.954641\n",
       " 2016.0    4.027576\n",
       " 2017.0    4.100909\n",
       "Name: average_rating, Length: 293, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不同年份书的平均评分情况\n",
    "data[\"average_rating\"].groupby(by=data[\"original_publication_year\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_grouped = data[\"average_rating\"].groupby(by=data[\"original_publication_year\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_x = rating_grouped.index\n",
    "_y = rating_grouped.values\n",
    "\n",
    "plt.figure(figsize=(20,8), dpi=80)\n",
    "plt.plot(range(len(_x)), _y)\n",
    "plt.xticks(list(range(len(_x)))[::10], np.round(_x[::10]), rotation=45) #或者 _x[::10].astype(int)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analyze]",
   "language": "python",
   "name": "conda-env-analyze-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
